1 citations · 1 across the 6 of their papers we have counts for
6 papers · 1 filter
OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
Bing Li, Wuqi Wang, Yanan Zhang +6
LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is ess…
FOCUS: Frequency-Optimized Conditioning of DiffUSion Models for mitigating catastrophic forgetting during Test-Time Adaptation
Gabriel Tjio, Jie Zhang, Xulei Yang +6
Test-time adaptation enables models to adapt to evolving domains. However, balancing the tradeoff between preserving knowledge and adapting to domain shifts remains challenging for…
DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches
Yun Xing, Yue Cao, Nhat Chung +6
Stereo depth estimation is a critical task in autonomous driving and robotics, where inaccuracies (such as misidentifying nearby objects as distant) can lead to dangerous situation…
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models
Ying Yang, Jie Zhang, Xiao Lv +3
While adversarial attacks on vision-and-language pretraining (VLP) models have been explored, generating natural adversarial samples crafted through realistic and semantically mean…
MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents
Yun Xing, Nhat Chung, Jie Zhang +5
Physical adversarial attacks in driving scenarios can expose critical vulnerabilities in visual perception models. However, developing such attacks remains challenging due to diver…
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments
Yue Cao, Yun Xing, Jie Zhang +5
Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models' vulnerability to deliberately…